This Thursday, the 22nd, a whole day of conferences related to machine learning, AI and data science is organized by the LIS (Laboratoire d’Informatique et Systèmes). A worthy program developed […]

This Thursday, the 22nd, a whole day of conferences related to machine learning, AI and data science is organized by the LIS (Laboratoire d’Informatique et Systèmes). A worthy program developed by the new computer lab of Aix-Marseille University and the CNRS, regrouping already existing structures (and collaborating with the Ecole Centrale) to obtain a critical size and to train future generations of metropolitan data scientists.In September 2018, the university will open its doors to the very first promotion of its computer science master’s degree dedicated to artificial intelligence and machine learning. « This program will mix computing, programming, mathematics and statistics, with more operational profiles on the computer side and theoretical on the math side » explained Thierry Artieres, in charge of this master.

No Difficulty Finding a Job

Like just about every student graduating from a computer science master’s degree, those from this class will have no difficulty finding a job, both in France and abroad. Especially because they will deal with a specialty, artificial intelligence, currently very much in demand. « We do not train enough AI specialists in France. In the academic world, the community is aspired by Google, Facebook, … That said, they do not necessarily expatriate, these companies open research centers elsewhere than in the US. And then there are companies that will grow in France, there will be good opportunities here « .

Massive investments are not required. Processing large volumes of data has a « computational cost », based on GPU cards or computational clusters, which remains reasonable. Especially since the big names of the sector started sharing. « A few years ago, all these companies were protective of their technologies. Today, the context has changed, they design large databases that they willingly disseminate. At our scale, it’s bulky, for them it’s not much. There is a logic in this: on deep learning, it is very difficult to have theoretical proofs, the experimental part becomes preponderant « . Hence the importance of having others, such as the LIF, testing datasets to identify useful conclusions for the entire deep learning community.